Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools
2021382 citationsIvano Lauriola, Alberto Lavelli et al.Neurocomputingprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Fabio Aiolli's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fabio Aiolli with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Aiolli more than expected).
This network shows the impact of papers produced by Fabio Aiolli. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fabio Aiolli. The network helps show where Fabio Aiolli may publish in the future.
Co-authorship network of co-authors of Fabio Aiolli
This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Aiolli.
A scholar is included among the top collaborators of Fabio Aiolli based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Fabio Aiolli. Fabio Aiolli is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Lauriola, Ivano, et al.. (2020). Exploring the feature space of character-level embeddings.. The European Symposium on Artificial Neural Networks. 637–642.
4.
Lauriola, Ivano, et al.. (2020). Automatic Detection of Cross-language Verbal Deception. eScholarship (California Digital Library). 1756–1762.2 indexed citations
5.
Lauriola, Ivano, et al.. (2020). DecOp: A Multilingual and Multi-domain Corpus For Detecting Deception In Typed Text.. Language Resources and Evaluation. 1423–1430.9 indexed citations
6.
Aiolli, Fabio, Mauro Conti, Ankit Gangwal, & Mirko Polato. (2019). Mind your wallet’s privacy identifying Bitcoin wallet apps and user’s actions through network traffic analysis. 1484–1491.2 indexed citations
Lauriola, Ivano, Michele Donini, & Fabio Aiolli. (2017). Learning dot-product polynomials for multiclass problems.. The European Symposium on Artificial Neural Networks.2 indexed citations
14.
Oneto, Luca, Nicolò Navarin, Michele Donini, et al.. (2016). Measuring the Expressivity of Graph Kernels through the Rademacher Complexity.. CINECA IRIS Institutial Research Information System (University of Genoa). 23–28.1 indexed citations
15.
Aiolli, Fabio & Mirko Polato. (2016). Kernel based collaborative filtering for very large scale top-N item recommendation.. The European Symposium on Artificial Neural Networks.5 indexed citations
16.
Bolón‐Canedo, Verónica, Michele Donini, & Fabio Aiolli. (2015). Feature and kernel learning.. Research Padua Archive (University of Padua).7 indexed citations
17.
Aiolli, Fabio & Michele Donini. (2014). Easy multiple kernel learning. Research Padua Archive (University of Padua).4 indexed citations
18.
Aiolli, Fabio, Giovanni Da San Martino, Alessandro Sperduti, & Markus Hagenbuchner. (2007). "Kernelized" Self-Organizing Maps for Structured Data. Research Padua Archive (University of Padua). 19–24.2 indexed citations
19.
Aiolli, Fabio & Alessandro Sperduti. (2005). Multiclass Classification with Multi-Prototype Support Vector Machines. Journal of Machine Learning Research. 6(28). 817–850.29 indexed citations
20.
Aiolli, Fabio & Alessandro Sperduti. (2003). Multi-prototype support vector machine. International Joint Conference on Artificial Intelligence. 541–546.3 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.